## AG 3: Teaching |

- The last tutorial will take place on July 16.

The course is the second part of a two semester course on Statistical Learning. The first part (WS 2003/2004) concentrated on chapters 1-10 of the book The Elements of Statistical Learning, Springer 2001, this follow up course consists of two parts. The first part will continue with chapters 11-14 of the book. The second part will deal with methods of Statistical Learning applied to problems in Bioinformatics. There will be two hours of lecture per week and one hour of tutorial (V2/Ü1).

This course covers a subject that is relevant for computer scientists in general as well as for other scientists involved in data analysis and modeling. It is not limited to the field of computational biology.

**Lecturer:** Jörg
Rahnenführer

**Tutor:** Jörg
Rahnenführer

**Course language:** English

Course: |
Weekly, Wednesdays 11-13, Building 46, Room 024. |

Tutorial: |
Once every two weeks (see below for dates), Fridays 16-18, Building 46, Room 021. |

Office hours: |
On appointment. |

The lecture is targeted to advanced students in math, computer science and science students with
mathematical background.

Prerequisites: Vordiplom in Mathematics or
Computer Science or equivalent. Students should know linear algebra and have
basic knowledge in statistics.

Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer 2001. Readers of the course are encouraged to acquire this book.

Lecture | Date | Topic |
---|---|---|

Lecture 1 | Wed April 21 | Repetition - Overview - Outlook |

Lecture 2 | Wed April 28 | Neural Networks (HTF chapter 11) |

Lecture 3 | Wed May 5 | Support Vector Machines (HTF chapter 12) |

Lecture 4 | Wed May 12 | Prototype Methods and Nearest-Neighbors (HTF chapter 13) |

Lecture 5 | Wed May 19 | Unsupervised Learning I (HTF chapter 14) |

Lecture 6 | Wed May 26 | Unsupervised Learning II (HTF chapter 14) |

Lecture 7 | Wed June 2 | Kernel Methods (HTF chapter 6) |

Lecture 8 | Wed June 9 | Low-level Analysis of Gene Expression Data |

Lecture 9 | Wed June 23 | Classification in Gene Expression Data |

Lecture 10 | Wed June 30 | Combining Gene Expression Data and Biological Network Data |

Lecture 11 | Wed July 7 | Classification of Protein Structures |

Lecture 12 | Wed July 14 | Learning with Mixtures of Trees |

Tutorial | Date | Topic | HW Assigned | HW Due |
---|---|---|---|---|

Tutorial 0 | Fri April 23 | Introduction to R - Repetition | HW 1 | |

Tutorial 1 | Fri May 7 | Linear Regression + Model Assessment | HW 2 | HW 1 |

Tutorial 2 | Fri May 14 | Neural Networks | HW 3 | HW 2 |

Tutorial 3 | Fri May 28 | Support Vector Machines | HW 4 | HW 3 |

Tutorial 4 | Fri June 11 | Nearest-Neighbors + Unsupervised Learning | HW 5 | HW 4 |

Tutorial 5 | Fri June 25 | Kernel Methods | HW 6 | HW 5 |

Tutorial 6 | Fri July 9 | Classification with gene expression data | HW 7 | HW 6 |

Tutorial 7 | Fri July 16 | Synopsis | HW 7 |

Both parts of this course fulfil the requirements for the curricula of computer science and bioinformatics as optional course with 6 resp. 4 credit points (Spezialvorlesung, 6 bzw. 4 Leistungspunkte).

50% of the homework points and final exam (most likely oral).